论文标题
人群数与细分注意卷积神经网络计数
Crowd counting with segmentation attention convolutional neural network
论文作者
论文摘要
深度学习在人群计数中占据了无可争议的统治地位。在本文中，我们提出了一种新型的卷积神经网络（CNN）结构，称为Segcrowdnet。尽管人群场景中的背景复杂，但PropoSesegrowdnet仍然适应性地突出了人头区域，并通过分割来抑制非头部区域。在引起注意机制的指导下，提出的Segcrowdnet对人头区域的关注更多，并自动编码高度精制的密度图。可以通过集成密度图来获得人群计数。为了适应人群数的变化，Segcrowdnet智能地将每个图像的人群计数分为几组。此外，在拟议的Segcrowdnet中学习和提取了多尺度特征，以克服人群的规模变化。为了验证我们提出的方法的有效性，在四个具有挑战性的数据集上进行了广泛的实验。结果表明，与最先进的方法相比，我们提出的Segcrowdnet具有出色的性能。
Deep learning occupies an undisputed dominance in crowd counting. In this paper, we propose a novel convolutional neural network (CNN) architecture called SegCrowdNet. Despite the complex background in crowd scenes, the proposeSegCrowdNet still adaptively highlights the human head region and suppresses the non-head region by segmentation. With the guidance of an attention mechanism, the proposed SegCrowdNet pays more attention to the human head region and automatically encodes the highly refined density map. The crowd count can be obtained by integrating the density map. To adapt the variation of crowd counts, SegCrowdNet intelligently classifies the crowd count of each image into several groups. In addition, the multi-scale features are learned and extracted in the proposed SegCrowdNet to overcome the scale variations of the crowd. To verify the effectiveness of our proposed method, extensive experiments are conducted on four challenging datasets. The results demonstrate that our proposed SegCrowdNet achieves excellent performance compared with the state-of-the-art methods.
